PDF(Pubmed)
Abstract：
The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.
摘要：
可穿戴传感器的使用，如惯性测量单元(IMU)，在健康相关领域，用于人类意图识别的机器学习已经大幅增长。然而,关于IMU数量和位置如何影响关节水平的人类运动意图预测（HMIP）的研究有限。这项研究的目的是分析IMU输入信号的各种组合，以最大化多个简单运动的机器学习预测精度。我们训练了随机森林算法，以使用各种传感器功能来预测这些运动中的未来关节角度。我们假设关节角度预测精度将随着附加到相邻身体节段的IMU的添加而增加,并且非相邻IMU不会增加预测精度。结果表明，将相邻IMU添加到当前关节角度输入并没有显着提高预测精度(1.92°的RMSE与脚踝处3.32°，8.78°vs.膝盖处12.54°，和5.48°vs.髋部9.67°)。此外,包括不相邻的IMU并没有提高预测精度(RMSE为5.35°与脚踝处5.55°，20.29°vs.膝部20.71°,和14.86°vs.髋部13.55°)。这些结果表明，随着当前关节角度输入的同时添加IMU，简单运动期间的未来关节角度预测并没有改善。
